🔥🔥| 解读1 | PillarNeSt微信解读2 | PillarNeSt微信解读2
PillarNeSt is a robust pillar-based 3D object detectors, which obtains 66.9%(SOTA without TTA/model ensemble) mAP and 71.6 % NDS on nuScenes benchmark.
Our paper has been officially accepted by the journal IEEE Transactions on Intelligent Vehicles (TIV) in April 2024.
- Environments
Python == 3.6
CUDA == 11.1
pytorch == 1.9.0
mmcls == 0.22.1
mmcv-full == 1.4.2
mmdet == 2.20.0
mmsegmentation == 0.20.2
mmdet3d == 0.18.1
- Data
Follow the mmdet3d to process the nuScenes dataset.
- Weights
Model weights are available at Google Drive and BaiduWangpan(PW: 1111).
Results on nuScenes val set. (15e + 5e means the last 5 epochs should be trained without GTsample)
Config | mAP | NDS | Schedule | weights | weights |
---|---|---|---|---|---|
PillarNeSt-Tiny | 58.8% | 65.6% | 15e+5e | Google Drive | Baidu |
PillarNeSt-Small | 61.7% | 68.1% | 15e+5e | Google Drive | Baidu |
PillarNeSt-Base | 63.2% | 69.2% | 15e+5e | Google Drive | Baidu |
PillarNeSt-Large | 64.3% | 70.4% | 18e+2e | Google Drive | Baidu |
Results on nuScenes test set (without any TTA/model ensemble).
Config | mAP | NDS |
---|---|---|
PillarNeSt-Base | 65.6 % | 71.3% |
PillarNeSt-Large | 66.9% | 71.6% |
Update:
- Update new CenterPointBBoxCoder
- add visualization
- add CenterPlusHead
- add HeightPillarFeatureNet
- add CenterPoint-Plus
- Small, Base, Large configs
- Upload weights to Baidu cloud
- Backbone code
If you have any questions, feel free to open an issue or contact us at maowx2017@fuji.waseda.jp.
If you find PillarNeSt helpful in your research, please consider citing:
@ARTICLE{10495196,
author={Mao, Weixin and Wang, Tiancai and Zhang, Diankun and Yan, Junjie and Yoshie, Osamu},
journal={IEEE Transactions on Intelligent Vehicles},
title={PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection},
year={2024},
volume={},
number={},
pages={1-10},
keywords={Three-dimensional displays;Point cloud compression;Feature extraction;Detectors;Object detection;Task analysis;Convolution;Point Cloud;3D Object Detection;Backbone Scaling;Pretraining;Autonomous Driving},
doi={10.1109/TIV.2024.3386576}}
Recently, our team also conduct some explorations into the application of multi-modal large language model (MLLM) in the field of autonomous driving:
Adriver-I: A general world model for autonomous driving
@article{jia2023adriver,
title={Adriver-i: A general world model for autonomous driving},
author={Jia, Fan and Mao, Weixin and Liu, Yingfei and Zhao, Yucheng and Wen, Yuqing and Zhang, Chi and Zhang, Xiangyu and Wang, Tiancai},
journal={arXiv preprint arXiv:2311.13549},
year={2023}
}
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